Articles from:November 2025

  • Congratulations to Billy Marshall for Major Research Prize

    Wednesday, November 26, 2025 | By

    Dr. Billy Marshall (Department of Mathematics and Statistics) and his team were recently awarded the 2025 Linda G. O’Bryant Noetic Sciences Research Prize (https://noetic.org/prize/).  They share the $100,000 prize with two other groups, all working on conscious AI.

    Of the 56 applicants and 8 finalists, the team lead by Dr. Marshall was selected for contributions in Evaluating Artificial Consciousness through Integrated Information Theory.  Over 3,000 researchers from around the world attended the online awards ceremony.

    We extend our congratulations to Dr. Marshall and his colleagues on their significant achievement.

  • Joshua Mac Intyre Masters Project Presentation: Thursday, November 27, 11:00 AM

    Wednesday, November 19, 2025 | By

    Joshua Mac Intyre, a Master of Science candidate in the Department of Mathematics and Statistics, will virtually present the Masters Research Project titled Nil Clean Group Rings over Metacyclic Groups on Thursday, November 27, 2025 at 11:00 AM.

    The examination committee includes Supervisor Dr. Yuanlin Li and Supervisory Committee Member Dr. Henryk Fukś.

    Students (both graduate and undergraduate) as well as other members of the Brock Community are invited to attend. A Microsoft Teams link to the meeting can be found here: Join the meeting.

    Keywords: Idempotent; group rings; nilpotent; nil clean; Peirce decomposition; Wedderburn-Artin; Metacyclic groups; Fermat numbers

    Abstract:  A ring is called nil clean if each element can be expressed as the sum of an idempotent and nilpotent. This presentation expounds on our work published in Nil clean group rings over metacyclic groups. We will assume at least an undergraduate understanding of group and ring theory but will provide some preliminary information on nil clean rings, group rings, and metacyclic groups. We will justify our comparison between the nil cleanness of ℤ2G, and any group ring RG, where R is a commutative ring and G is a finite group. This comparison will lead to an analysis of ℤ2G, for G up to an order of 20. Then, when considering a nil clean group ring RG over a metacyclic group G, we were able to reduce to the case where G = < a, b | a^{n} = b^{m} = 1, b^{-1}ab = a^{r} >, with m = 2k, n odd, and the center being trivial. We try to break it down a little further to when n is a prime power, calculating n for each m up to 16, and we verify whether most of these candidate group rings are indeed nil clean. Finally, we discuss the results of our investigation and a connection to Fermat numbers.

  • Mathematics and Statistics Seminar Series, Dr. Bernhard Spangl

    Wednesday, November 05, 2025 | By

    Students, faculty, and staff are invited to attend the upcoming event in the Mathematics and Statistics Seminar Series with speaker Dr. Bernhard Spangl on Tuesday, November 11, from 1:00 PM to 2:00 PM.  The talk is entitled Active learning: blending design of experiments and supervised learning

    Abstract

    In this talk I will focus on two research topics: (i) sample size estimation in balanced ANOVA models and (ii) query-by-committee active learning in regression scenarios. Their common aim is to minimize the sample size.

    First, we consider balanced one-way, two-way, and three-way ANOVA models to test the hypothesis that the fixed factor A has no effect. The other factors are fixed or random. We determine the noncentrality parameter for the exact F-test, describe its minimal value by a sharp lower bound, and thus we can guarantee the worst-case power for the F-test. These results allow us to compute the minimal sample size, i.e. the minimal number of experiments needed. Additionally, we provide a structural result for the minimal sample size that we call “pivot” effect (cf. also Spangl et al., 2021).

    Second, we discuss the problem of active learning in regression scenarios. In active learning, the goal is to provide criteria that the learning algorithm can employ to improve its performance by actively selecting data that are most informative. Active learning is usually thought of as being a sequential process where the training set is augmented one data point at a time. We restrict ourselves to a pool-based sampling scenario and investigate a committee-based approach as query strategy for actively selecting instantiations of the input variables x that should be labelled and incorporated into the training set using a real chemometric data set.

     

    Registration link 

    Mathematics and Statistics Seminar Series: Active learning: blending design of experiments and supervised learning with Dr. Bernhard Spangl – ExperienceBU